Parallelization of a Denoising Algorithm for Tonal Bioacoustic Signals Using OpenACC Directives

Jorge Castro; Esteban Meneses

doi:10.1109/IWOBI.2018.8464129

Parallelization of a Denoising Algorithm for Tonal Bioacoustic Signals Using OpenACC Directives

Jorge Castro
, Esteban Meneses

National Center of High Technology (CeNAT-CONARE)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

Automatic segmentation and classification methods for bioacoustic signals enable real-time monitoring, population estimation, as well as other important tasks for the conservation, management, and study of wildlife. These methods normally require a filter or a denoising strategy to enhance relevant information in the input signal and avoid false positive detections. This denoising stage is usually the performance bottleneck of such methods. In this paper, we parallelize a denoising algorithm for tonal bioacoustic signals using mainly OpenACC directives. The implemented program was executed in both multicore and GPU architectures. The proposed parallelized algorithm achieves a higher speedup on GPU than CPU, leading to a 10.67 speedup compared to the original sequential algorithm in C++.

Original language	English
Title of host publication	2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Print)	9781538675069
DOIs	https://doi.org/10.1109/IWOBI.2018.8464129
State	Published - 12 Sep 2018
Event	2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - San Carlos, Costa Rica Duration: 18 Jul 2018 → 20 Jul 2018

Publication series

Name	2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - Proceedings

Conference

Conference	2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018
Country/Territory	Costa Rica
City	San Carlos
Period	18/07/18 → 20/07/18

Keywords

Bioacoustics
Denoising
Graphic Processing Unit (GPU)
OpenACC
Parallel computing

Access to Document

10.1109/IWOBI.2018.8464129

Cite this

Castro, J., & Meneses, E. (2018). Parallelization of a Denoising Algorithm for Tonal Bioacoustic Signals Using OpenACC Directives. In 2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - Proceedings Article 8464129 (2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IWOBI.2018.8464129

Castro, Jorge ; Meneses, Esteban. / Parallelization of a Denoising Algorithm for Tonal Bioacoustic Signals Using OpenACC Directives. 2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. (2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - Proceedings).

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title = "Parallelization of a Denoising Algorithm for Tonal Bioacoustic Signals Using OpenACC Directives",

abstract = "Automatic segmentation and classification methods for bioacoustic signals enable real-time monitoring, population estimation, as well as other important tasks for the conservation, management, and study of wildlife. These methods normally require a filter or a denoising strategy to enhance relevant information in the input signal and avoid false positive detections. This denoising stage is usually the performance bottleneck of such methods. In this paper, we parallelize a denoising algorithm for tonal bioacoustic signals using mainly OpenACC directives. The implemented program was executed in both multicore and GPU architectures. The proposed parallelized algorithm achieves a higher speedup on GPU than CPU, leading to a 10.67 speedup compared to the original sequential algorithm in C++.",

keywords = "Bioacoustics, Denoising, Graphic Processing Unit (GPU), OpenACC, Parallel computing",

author = "Jorge Castro and Esteban Meneses",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 ; Conference date: 18-07-2018 Through 20-07-2018",

year = "2018",

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doi = "10.1109/IWOBI.2018.8464129",

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series = "2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - Proceedings",

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Castro, J & Meneses, E 2018, Parallelization of a Denoising Algorithm for Tonal Bioacoustic Signals Using OpenACC Directives. in 2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - Proceedings., 8464129, 2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018, San Carlos, Costa Rica, 18/07/18. https://doi.org/10.1109/IWOBI.2018.8464129

Parallelization of a Denoising Algorithm for Tonal Bioacoustic Signals Using OpenACC Directives. / Castro, Jorge; Meneses, Esteban.
2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. 8464129 (2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Parallelization of a Denoising Algorithm for Tonal Bioacoustic Signals Using OpenACC Directives

AU - Castro, Jorge

AU - Meneses, Esteban

PY - 2018/9/12

Y1 - 2018/9/12

N2 - Automatic segmentation and classification methods for bioacoustic signals enable real-time monitoring, population estimation, as well as other important tasks for the conservation, management, and study of wildlife. These methods normally require a filter or a denoising strategy to enhance relevant information in the input signal and avoid false positive detections. This denoising stage is usually the performance bottleneck of such methods. In this paper, we parallelize a denoising algorithm for tonal bioacoustic signals using mainly OpenACC directives. The implemented program was executed in both multicore and GPU architectures. The proposed parallelized algorithm achieves a higher speedup on GPU than CPU, leading to a 10.67 speedup compared to the original sequential algorithm in C++.

AB - Automatic segmentation and classification methods for bioacoustic signals enable real-time monitoring, population estimation, as well as other important tasks for the conservation, management, and study of wildlife. These methods normally require a filter or a denoising strategy to enhance relevant information in the input signal and avoid false positive detections. This denoising stage is usually the performance bottleneck of such methods. In this paper, we parallelize a denoising algorithm for tonal bioacoustic signals using mainly OpenACC directives. The implemented program was executed in both multicore and GPU architectures. The proposed parallelized algorithm achieves a higher speedup on GPU than CPU, leading to a 10.67 speedup compared to the original sequential algorithm in C++.

KW - Bioacoustics

KW - Denoising

KW - Graphic Processing Unit (GPU)

KW - OpenACC

KW - Parallel computing

UR - https://www.scopus.com/pages/publications/85054483188

U2 - 10.1109/IWOBI.2018.8464129

DO - 10.1109/IWOBI.2018.8464129

M3 - Contribución a la conferencia

AN - SCOPUS:85054483188

SN - 9781538675069

T3 - 2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - Proceedings

BT - 2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018

Y2 - 18 July 2018 through 20 July 2018

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Castro J, Meneses E. Parallelization of a Denoising Algorithm for Tonal Bioacoustic Signals Using OpenACC Directives. In 2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. 8464129. (2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - Proceedings). doi: 10.1109/IWOBI.2018.8464129

Parallelization of a Denoising Algorithm for Tonal Bioacoustic Signals Using OpenACC Directives

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